Physics in medicine and biology
-
Static whole-body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single-bed-coverage limiting the axial field-of-view to ~15-20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. ⋯ Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole body. In addition, the total acquisition length can be reduced from 45 to ~35 min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error and the CNR metrics, resulting in enhanced task-based imaging.
-
In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15-20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. ⋯ Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30 min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole-body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection.